1 citations · 1 across the 8 of their papers we have counts for
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LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs
Paolo Gajo, Domenic Rosati, Hassan Sajjad +1
Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a pr…
Dependency Parsing is More Parameter-Efficient with Normalization
Paolo Gajo, Domenic Rosati, Hassan Sajjad +1
Dependency parsing is the task of inferring natural language structure, often approached by modeling word interactions via attention through biaffine scoring. This mechanism works…
Improving Consistency in Large Language Models through Chain of Guidance
Harsh Raj, Vipul Gupta, Domenic Rosati +1
Consistency is a fundamental dimension of trustworthiness in Large Language Models (LLMs). For humans to be able to trust LLM-based applications, their outputs should be consistent…
Resolving Lexical Bias in Model Editing
Hammad Rizwan, Domenic Rosati, Ga Wu +1
Model editing aims to modify the outputs of large language models after they are trained. Previous approaches have often involved direct alterations to model weights, which can res…
Representation Noising: A Defence Mechanism Against Harmful Finetuning
Domenic Rosati, Jan Wehner, Kai Williams +7
Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release o…
Long-form evaluation of model editing
Domenic Rosati, Robie Gonzales, Jinkun Chen +6
Evaluations of model editing currently only use the `next few token' completions after a prompt. As a result, the impact of these methods on longer natural language generation is l…
Immunization against harmful fine-tuning attacks
Domenic Rosati, Jan Wehner, Kai Williams +4
Large Language Models (LLMs) are often trained with safety guards intended to prevent harmful text generation. However, such safety training can be removed by fine-tuning the LLM o…